User effort minimization through adaptive diversification

Hasan, Mahbub; Kashyap, Abhijith; Hristidis, Vagelis; Tsotras, Vassilis J.

doi:10.1145/2623330.2623610

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…Diversification Diversification of query results has attracted a lot of attention recently as a method for improving the quality of results by balancing similarity (relevance) to a query q and dissimilarity among results [12,24,25,30,52]. Diversification has also been considered in keyword search over graphs and databases, where the result is usually a subgraph that contains the set of query keywords.…”

Section: Related Workmentioning

confidence: 99%

Diversified spatial keyword search on RDF data

et al. 2020

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The abundance and ubiquity of RDF data (such as DBpedia and YAGO2) necessitate their effective and efficient retrieval. For this purpose, keyword search paradigms liberate users from understanding the RDF schema and the SPARQL query language. Popular RDF knowledge bases (e.g., YAGO2) also include spatial semantics that enable location-based search. In an earlier location-based keyword search paradigm, the user inputs a set of keywords, a query location, and a number of RDF spatial entities to be retrieved. The output entities should be geographically close to the query location and relevant to the query keywords. However, the results can be similar to each other, compromising query effectiveness. In view of this limitation, we integrate textual and spatial diversification into RDF spatial keyword search, facilitating the retrieval of entities with diverse characteristics and directions with respect to the query location. Since finding the optimal set of query results is NP-hard, we propose two approximate algorithms with guaranteed quality. Extensive empirical studies on two real datasets show that the algorithms only add insignificant overhead compared to non-diversified search, while returning results of high quality in practice (which is verified by a user evaluation study we conducted).

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Section: Related Workmentioning

confidence: 99%

Diversified spatial keyword search on RDF data

et al. 2020

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“…Ces conclusions corroborent les travaux de (Jones, 2010) selon lesquels le mode de présentation ainsi que la diversité des recommandations influent sur la confiance accordée au système par les utilisateurs. Ainsi, bien que la diversité dans les recommandations corresponde à un besoin réel, la quantité de diversité à introduire, le mode de présentation et le moment où la diversité doit être maximisée ou minimisée posent de nombreuses questions s'inscrivant actuellement au coeur des recherches (Hasan et al, 2014), (Liang et al, 2014).…”

Section: Définition Et Rôleunclassified

Modéliser la diversité au cours du temps pour détecter le contexte dans un service de musique en ligne

L'huillier¹,

Castagnos²,

Boyer³

2016

Techniques et sciences informatiques

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LORIA-Campus scientifique B.P. 239 54506 Vandoeuvre-lès-Nancy CEDEX {amaury.lhuillier,sylvain.castagnos,anne.boyer}@loria.fr RÉSUMÉ. De nombreuses études ont démontré que la prise en compte du contexte améliore la qualité des systèmes de recommandation. Cependant, les méthodes traditionnelles permettent d'inférer le contexte à l'aide de données personnelles (localisation, date, âge, etc.). Dans ce papier, nous proposons de détecter automatiquement les changements de contexte, sans connaissance sur les utilisateurs (contexte explicite), mais en fonction des caractéristiques communes aux items consultés (contexte implicite). Pour ce faire, nous proposons un modèle formel capable d'établir une correspondance entre les variations de diversité au cours du temps dans les parcours des utilisateurs et les changements de contexte. Ce modèle a été testé sur un corpus musical de plus de 200 000 écoutes. Pour valider la pertinence de notre modèle, nous avons cherché à retrouver des événements à partir des changements de contexte détectés : notre modèle a ainsi permis de retrouver 88% des fins de session. ABSTRACT. Many studies have shown that taking into account the context improves the quality of recommender systems. However, traditional methods infer the context using personal data (location, date, age, etc.). In this paper, we propose to automatically detect context changes, without knowledge on users (explicit context), but based on common features of consulted items (implicit context). To do this, we propose a formal model which can establish a correspondence between the variation of diversity over time within the paths of users and context changes. This model has been tested on a musical corpus of more than 200,000 tracks. To validate the relevance of our model, we sought to retrieve events from the detected changes of context: our model has recovered 88% of session ends.

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“…The problem of returning a selective subset of answers to users has been studied by many research communities in many contexts, such as recommender systems [18,6] and top-k queries with diversification [2,3,14]. For many applications, the answers can be modeled as points on an elevation surface over an underlying domain.…”

Section: Introductionmentioning

confidence: 99%

Finding diverse, high-value representatives on a surface of answers

Gao

Agarwal

et al. 2017

Proc. VLDB Endow.

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In many applications, the system needs to selectively present a small subset of answers to users. The set of all possible answers can be seen as an elevation surface over a domain, where the elevation measures the quality of each answer, and the dimensions of the domain correspond to attributes of the answers with which similarity between answers can be measured. This paper considers the problem of finding a diverse set of k high-quality representatives for such a surface. We show that existing methods for diversified top-k and weighted clustering problems are inadequate for this problem. We propose k-DHR as a better formulation for the problem. We show that k-DHR has a submodular and monotone objective function, and we develop efficient algorithms for solving k-DHR with provable guarantees. We conduct extensive experiments to demonstrate the usefulness of the results produced by k-DHR for applications in computational lead-finding and fact-checking, as well as the efficiency and effectiveness of our algorithms.

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User effort minimization through adaptive diversification

Cited by 9 publications

References 23 publications

Diversified spatial keyword search on RDF data

Diversified spatial keyword search on RDF data

Modéliser la diversité au cours du temps pour détecter le contexte dans un service de musique en ligne

Finding diverse, high-value representatives on a surface of answers

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